Analysis on Safety Performance Improvement of Smart Metering System Based on Big data

This paper aims to build an active defense system by integrating big data technology to address the security risks faced by existing smart metering systems, such as data tampering, network attacks, and delayed anomaly detection, especially the problems of insufficient encryption algorithm strength, access control vulnerabilities, and low real-time response efficiency. The research method includes four technical levels: First, based on the Hadoop+Spark architecture, real-time collection and cleaning of millions of data per second are achieved. Secondly, the improved random forest algorithm (the feature selection dimension is expanded to 32 items) is used for millisecond-level anomaly detection. Then, the LSTM time series prediction model (the number of hidden layer nodes is 128) is used to warn of potential attacks 15 minutes in advance. Finally, the blockchain smart contract is combined to complete the dynamic verification of permissions (the transaction processing speed is increased to 1500TPS). After actual testing in industrial scenarios, the accuracy of system anomaly recognition has increased to 98.4%, the response delay has been compressed to 1.1 seconds, the attack prediction accuracy has reached 92.3%, and the defense mechanism has successfully blocked 94.6% of SQL injections and DDoS attacks. Research shows that this multimodal fusion solution effectively solves the defects of passive defense of traditional metering systems and provides a verifiable technical paradigm for the security protection of critical infrastructure.